Image processing apparatus and method for estimating orientation

ABSTRACT

Method of estimating orientation of objects disposed on a plane from video images of a scene taken by a video camera. Includes receiving for each object, object tracking data, providing a position of the object on the plane in the image with respect to time, determining from the object tracking data basis vectors associated with an objects, each basis vector corresponding to a factor, which can influence the orientation of the object and each basis vector being related to the movement or location of the one or more objects, and combining the basis vectors in accordance with a blending function to calculate an estimate of the orientation of the object on the plane, the blending function including blending coefficients which determine a relative magnitude of each basis vector used in the blending function.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to UK Patent Application No. GB0723716.7 filed on Dec. 4, 2007, the entirety being incorporated hereinby reference.

FIELD OF THE INVENTION

The present invention relates to apparatus for and methods of estimatingan orientation of one or more of a plurality of objects disposed on aplane, from one or more video images of a scene, which includes theobjects on the plane produced from a view of the scene by a videocamera. The present invention also relates to apparatus and methods ofgenerating a three dimensional representation of a scene which includesthe plurality of objects disposed on the plane.

In one example, the objects are human sports players on a sports field,such as football players on a football pitch.

BACKGROUND OF THE INVENTION

There are many situations in which it is desirable to try to extractinformation from data captured during live action events, such assporting events, to provide further insight into what is taking place.Such information can be used by broadcasters to enhance a viewingexperience provided to viewers watching the live action event.Techniques that provide the extraction of information from captured datainclude, for example, three dimensional tracking of where a ball landedin relation to the line on a tennis court to determine whether the ballshould be called in or out. Another well known example is extrapolatinga projected path of a ball which has struck a batsman on his pads in agame of cricket in order to determine if he should be given out legbefore wicket.

Another approach is to process video images of a scene to identifyobjects such as human beings within a scene. In many cases, such astelevised sporting events, processing video images in this way can bemore convenient, because the video images are already available.However, extracting information from video images is difficult, firstlybecause the data is captured in only two dimensions by a camera andsecondly because the processing of the video images to extract desiredinformation can be computationally intensive and error prone, becauseobjects or players must be recognised from a low or variable resolutionrepresentation, due to higher resolution images being provided forimages captured near the camera and lower resolution images beingcaptured further from the camera. Furthermore, a high degree ofvariability in the nature of the movement of humans, makes recognitionof players difficult. Other image processing techniques require manycameras to be available in order to capture video images of a subjectfrom several different angles. In Moeslund et al. 2006, “A survey ofadvances in vision-based human motion capture and analysis”, a review ofacademic literature is presented which examines the available techniquesfor estimating human motion from captured image data. As discussed inthis paper, most techniques require controlled studio captureconditions, high-resolution imagery, multiple cameras (typically atleast four) and have very high computational requirements.

SUMMARY OF THE INVENTION

According to a first aspect there is provided method of estimating anorientation of one or more of a plurality of objects disposed on aplane, from one or more video images of a scene, which includes theobjects on the plane produced from a view of the scene by a videocamera. The method comprises receiving for each of the one or moreobjects, object tracking data, which provides a position of the objecton the plane in the video images with respect to time, determining fromthe object tracking data a plurality of basis vectors associated with atleast one of the objects, each basis vector corresponding to a factor,which can influence the orientation of the object and each basis vectorbeing related to the movement or location of the one or more objects,and combining the basis vectors in accordance with a blending functionto calculate an estimate of the orientation of the object on the plane,the blending function including blending coefficients which determine arelative magnitude of each basis vector used in the blending function.

Using the estimate of the orientation of each object, a threedimensional model can be generated which can, as far as possible,represent the scene of the objects on the plane. For example, the scenemay be that of a sporting event such as a football match, the objectsbeing football players. The method can generate an estimate of theorientation of each of the players on the plane of the sports pitch. Theestimate of the orientation can be combined with the object trackingdata for each player to generate a three dimensional representation,which models, as far as possible, the real football match as captured bythe video camera.

The present invention allows the orientation of one or more of aplurality of objects to be determined from tracking data indicating themovement and location of the one or more objects. The present inventionrecognises that although the tracking data provides data indicating theposition and movement of the objects, further useful information can bederived from this tracking data, if a number of assumptions are madeabout the way in which an object will be orientated based on theposition and movement of the object itself and other objects. Suchassumptions provide the basis vectors, which indicate how theorientation of the object may be influenced in view of the informationfrom the tracking data. The basis vectors are then combined inaccordance with a basis function, which is able to give an appropriateweight to each basis vector by virtue of the blending coefficients toprovide an orientation value for the object. The blending functioneffectively produces an estimate of which direction in real life theobject will be orientated on the plane using a combination of thevarious basis vectors. Therefore, an orientation value for the object isprovided without the requirement for intensive image processing and in amanner which can be performed in real or pseudo real time.

In accordance with one embodiment of the invention, the basis vectorsinclude an object velocity vector derived from an estimated velocity ofthe one or more objects determined from the tracking data. In thisembodiment, therefore one of the factors that influences the orientationof the object is the object's velocity. This basis factor recognises thefact that the direction an object is facing will depend to some extenton the speed at which the object is moving.

In accordance with another example of the invention, the basis vectorsinclude a centre of attention vector determined by a centre of gravityof the estimated position of the one or more objects derived from thetracking data. This basis factor recognises the fact that the centre ofgravity, which is the mean position of all the objects is likely to be agood approximation of the current location of a ball or where the mostimportant event is occurring. The “centre of attention” will also havean influence on the direction that the object is facing.

In accordance with another example, the basis vectors include a centreof attention velocity vector, providing a speed and direction of themotion of the centre of gravity on the plane.

In accordance with another example of the invention, the blendingcoefficients include an object speed coefficient set in dependence uponthe estimated speed of the one or more objects. In accordance with thisexample, it is recognised that the degree to which each basis vectorwill influence the overall orientation value depends, amongst otherthings, on the speed of the object. An object running at full speed ornear full speed is likely to be orientated toward the direction oftravel, rather than any other direction.

In accordance with another example, the blending coefficients include acentre of attention distance coefficient set in dependence upon anestimated distance of the one or more objects from the centre ofattention derived from the tracking data. In accordance with thisexample, it is ensured that the orientation value of the object does nottend to an unstable value if the object is at or near the centre ofattention.

Various further aspects and features of the present invention aredefined in the appended claims, which include an apparatus for and amethod of generating a three dimension representation of a scene, whichincludes a plurality of objects disposed on a plane. The aspects alsoinclude and a data carrier having a recordable medium on which there isrecorded information signals representing a computer program forperforming the method according to any of the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described by way ofexample with reference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram of an object tracking system in accordancewith an embodiment of the present invention;

FIG. 2 is a schematic block diagram of the content processingworkstation shown in FIG. 1;

FIG. 3 is a schematic illustration of a three dimensional modelgeneration processor, utilising the information generated by the contentprocessing work station shown in FIGS. 1 and 2;

FIG. 4 is a flow diagram of a method of object tracking in accordancewith embodiments of the present invention;

FIGS. 5A and 5B are schematic diagrams of object tracking in accordancewith an embodiment of the present invention;

FIG. 6 is a schematic illustration of a stick model of a synthesisedfigure;

FIG. 7 is a three dimensional model of a football match, which isgenerated using the information generated by the content processingworkstation of FIGS. 1 and 2;

FIG. 8 is a schematic illustration of a pixelated image of a footballplayer with a fore ground box, according to an example embodiment of thepresent invention, with the legs of the player at full separation, andFIG. 9 provides a corresponding schematic illustration for the legs at aminimum separation;

FIG. 10 provides an example illustration of a mask for isolating aninner and outer region around a players legs;

FIG. 11( a) is a schematic illustration of a pixelated image of afootball player with the bounding box of FIG. 10 superimposed on thelegs of the player at full separation, before an adjustment of theposition of the bounding box, FIG. 11( b) shows a correspondingschematic illustration with the bounding box after the adjustment in theposition, FIG. 11( c) is a corresponding illustration of a pixelatedimage of the football player with the bounding box of FIG. 10superimposed on the legs of the player at minimum separation, before anadjustment of the position of the bounding box and FIG. 11( d) shows acorresponding illustration after an adjustment of the position of thebounding box;

FIG. 12 is a graphical representation of a relative degree of motionwith respect to gait cycle for a detected gait and a representativegait;

FIG. 13 provides an illustrative plot of relative degrees of motionagainst percentage of gait cycle for an example video image of a player;

FIG. 14 is a schematic representation of a football player for threeexample poses with respect to the graphical plot shown in FIG. 13;

FIG. 15 provides an illustrative plot of relative degrees of motionagainst percentage of gait cycle for a synthesised model of the playerof FIG. 14;

FIG. 16 is a schematic representation of a synthesised model of afootball player for three example poses with respect to the graphicalplot shown in FIG. 15;

FIG. 17 is a graphical plot of degrees of motion against percentage ofgait cycle for knee rotation of a typical human gait for three gaitmodels of walking, running and sprinting;

FIG. 18 is a graphical plot of degrees of motion against percentage ofgait cycle for ankle rotation of a typical human gait for three gaitmodels of walking, running and sprinting;

FIG. 19 is a graphical plot of degrees of motion against percentage ofgait cycle for hip rotation of a typical human gait for three gaitmodels of walking, running and sprinting;

FIG. 20 is a graphical plot of foreground variation frequency againstspeed of movement, measured for all players (except the goal keeper) inan example football match;

FIG. 21 is a graphical plot of gait frequency against speed of movementfor the player corresponding to the example shown in FIG. 20, whichprovides a gait transfer function between speed and gait frequency;

FIG. 22 is an illustration of a video image captured from a scene, whichshows a football match with players to be tracked;

FIG. 23A is an illustration of a video image which has been processed inaccordance with the present technique to produce a background model, bytaking the mean and FIG. 23B shows the background model when consideringthe variance;

FIG. 24 is an illustration of a video image which has been processed inaccordance with the present technique to show tracked positions ofplayers;

FIG. 25 is an illustration of two video images which have been capturedfrom two different cameras, one for each side of the pitch and anillustration of a virtual representation of the football match in whichthe position of the players is tracked with respect to time;

FIG. 26 is a representation of a video image of a football match inwhich the players which have been tracked in accordance with the presenttechnique are labelled;

FIG. 27 is a three dimensional representation of a virtual model of afootball match in which a view of the match can be changed; and

FIG. 28 is a flow diagram illustrating the player orientation processaccording to the present technique.

DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 1 shows a schematic diagram of an object tracking system inaccordance with embodiments of the present invention. In the embodimentshown in FIG. 1, the objects to be tracked are football players (notshown) on a football pitch 30. High definition (HD) video images (1920by 1080 pixels) of the pitch 30 are captured by one or more highdefinition cameras. Although, embodiments of the present invention canbe used to track objects in video images from more than one camera, insome examples only a single camera is used. As will be appreciated, HDcameras are expensive, so that using only a single camera can reduce anamount of expense required to implement systems which utilise thepresent technique. However, using only a single camera provides only asingle two dimensional view of a scene within which the objects aredisposed. As a result tracking of the objects within the scenerepresented by the video images can be more difficult, because occlusionevents, in which one object obscures another are more likely. Such asingle camera 20 example is shown in FIG. 1, although as illustrated bycamera 22.1, 22.2 optionally two cameras can be used, each pointing at adifferent half of the football pitch.

In FIG. 1, a video camera 20 is disposed at a fixed point within thefootball stadium and arranged to communicate signals representing videoimages captured by the camera 20 to a content processing workstation 10,which carries out image processing and other operations so as to trackthe position of the players on the pitch with respect to time. Datarepresenting the position of the players with respect to time is thenlogged so that metadata and match statistics can be generated such asthe length of time a particular player spent in a particular part of thepitch, how far each player ran and the like. The data representing theposition of the players with respect to time forms path data for eachplayer, which relates to the path that each player has taken within thevideo images. The path data is generated with respect to a threedimensional model of the football pitch (object plane) in order toprovide information associated with movement of the players with respectto their position on the pitch, which are not readily apparent from the(two dimensional) video images. This generated path data can then beused to enhance a viewing experience for a viewer when footage of thefootball match is transmitted via a suitable medium to the viewer or toassist a coach when coaching the football team. The tracking of objectssuch as players on the pitch 30 will be described in more detail below.

As shown in FIG. 1, the content processing workstation 10 generates atan output 12 tracking data, player pose data and player orientationdata. The tracking data is representative of the position of each of theplayers on each team on the football field with respect to time. Thus,from the tracking data a relative motion of each player in terms of avelocity (providing speed and direction on the football field) can begenerated. From this information the player pose data can be estimated.The player pose data results from a process which estimates a pose ofeach player, as the players appear on the real football pitch viewedfrom the camera which produced the video images. The video images areused to match the pose of the player as the player moves around thefootball pitch. The generation of the pose estimation will be explainedin more detail shortly. Similarly, the player orientation is generatedto estimate the relative orientation of the player on the footballpitch. Thus, the orientation corresponds to a relative angular positionwhich the player is facing at any one time on the football field. Theplayer orientation data is extracted from the tracking data. Thegeneration of the tracking data, the player pose data and the playerorientation data is performed within the content processing workstation10 by different processors or different software modules as representedin FIG. 2.

In FIG. 2 a player tracking process module 40 receives a signalrepresentative of the video images captured from the camera 20 or indeedthe other video cameras 22.1, 22.2. The player tracking processgenerates the player tracking data, which is output by an output channel12.1. The tracking data is also received by a player orientationestimation process module 42, which uses the tracking data to generate arelative orientation of the player, in terms of the direction which theplayer is facing, from the tracking data for any position on thefootball field identified by that tracking data. The player orientationestimation process will be described in more detail shortly.

A player pose estimation module 44 receives both the video images fromthe camera 20 and on a further channel 46 the tracking data produced bythe player tracking module 40. As will be explained shortly, the playerpose estimation module generates a player pose estimation for eachplayer representing the relative posture of the player with respect totime for each position on the field identified by the tracking data. Theplayer orientation data and the player pose estimation data are outputon channels 12.2, 12.3 to form collectively the data output on thechannel 12 as shown in FIG. 1. It will be appreciated that the systemand method of object tracking, orientation and pose according toembodiments of the present invention need not be limited to trackingplayers on a football pitch. For example, players of other team sportssuch as rugby, cricket, American football, ice hockey, basketball andthe like could be tracked.

According to the present technique, the video images, which aregenerated using the HD video camera 20 are arranged to capture a view ofthe whole pitch, so that the players on the pitch can be tracked. Thusthe whole pitch is captured from a static position of the camera 20,although as mentioned above, more than one camera could be used, inorder to capture the whole pitch. In one example, as mentioned above,the two cameras 22.1, 22.2 may be used each of which is directed atdifferent halves of the pitch. In this example, the video imagesgenerated by each camera may be stitched together by the contentprocessing workstation 10 as described in United Kingdom PatentApplication No. 0624410.7 so as to form ultra high resolution videoimages. In this embodiment, after undergoing the stitching process, theoutput from the camera cluster can be thought of as a single ultra-highresolution image.

The advantages of the ultra-high definition arrangement are numerousincluding the ability to highlight particular features of a playerwithout having to optically zoom and therefore affecting the overallimage of the stadium. Furthermore, the automatic tracking of an objectis facilitated because the background of the event is static and thereis a higher screen resolution of the object to be tracked.

As shown in FIG. 3, the data received from channel 12 is in oneembodiment fed to a model generation processor 50. The model generationprocessor receives the tracking data, the player orientation estimationdata and the player pose data and generates a three-dimensional modelwhich represents the football pitch with each of the players representedby synthesised models providing a like representation of each player.Thus, the three-dimensional representation is as far as possible areflection of the football match as observed through the video camerarepresented by the video images received from the camera 20.

Object Tracking

Object tracking in accordance with embodiments of the present inventionwill now be described with reference to FIGS. 4, 5 and 6.

FIG. 4 shows a flowchart of a method of object tracking in accordancewith embodiments of the present invention. In order to track an object,a background model is constructed from those parts of the received videothat are detected as being substantially static over a predeterminednumber of frames. In a first step S30 the video image received from thecamera 20, which represents the football pitch is processed to constructthe background model of the image. The background model is constructedin order to create a foreground mask which assists in identifying andtracking the individual players. The background model is formed at stepS30 by determining for each pixel a mean of the pixels and a variance ofthe pixel values between successive frames in order to build thebackground model. Thus, in successive frames where the mean value of thepixels do not change greatly then these pixels can be identified asbackground pixels in order to identify the foreground mask.

Such a background/foreground segmentation is a process which is known inthe field of image processing and the present technique utilises analgorithm described in document by Manzanera and Richefeu, and entitled“A robust and Computationally Efficient Motion Detection Algorithm Basedon Σ-Δ Background Estimation”, published in proceedings ICVGIP, 2004.However, the present technique should not be taken as being limited tothis known technique and other techniques for generating a foregroundmask with respect to a background model for use in tracking are alsoknown.

It will be appreciated that, in the case where the field of view of thevideo camera encompasses some of the crowd, the crowd is unlikely to beincluded in the background model as they will probably be moving around.This is undesirable because it is likely to increase a processing loadon the Cell processor when carrying out the object tracking as well asbeing unnecessary as most sports broadcasters are unlikely to beinterested in tracking people in the crowd.

In an embodiment of the present invention, the background model isconstructed at the start of the game and can even be done before playerscome onto the pitch. Additionally, the background model can berecalculated periodically throughout the game so as to take account ofany changes in lighting condition such as shadows that may varythroughout the game.

In step S40, the background model is subtracted from the incoming imagefrom the camera to identify areas of difference. Thus the backgroundmodel is subtracted from the image and the resultant image is used togenerate a mask for each player. In step S45, a threshold is createdwith respect to the pixel values in a version of the image which resultswhen the background model has been subtracted. The background model isgenerated by first determining the mean of the pixels over a series offrames of the video images. From the mean values of each of the pixels,the variance of each of the pixels can be calculated from the frames ofthe video images. The variance of the pixels is then used to determine athreshold value, which will vary for each pixel across all pixels of thevideo images. For pixels, which correspond to parts of the image, wherethe variance is high, such as parts which include the crowd, thethreshold can be set to a high value, whereas the parts of the image,which correspond to the pitch will have a lower threshold, since thecolour and content of the pitch will be consistently the same, apartfrom the presence of the players. Thus, the threshold will determinewhether or not a foreground element is present and therefore aforeground mask can correspondingly be identified. In step S50 a shapeprobability based on a correlation with a mean human shape model is usedto extract a shape within the foreground mask. Furthermore, colourfeatures are extracted from the image in order to create a colourprobability mask, in order to identify the player, for example from thecolour of the player's shirt. Thus the colour of each team's shirts canbe used to differentiate the players from each other. To this end, thecontent processing workstation 10 generates colour templates independence upon the known colours of each football team's team kit.Thus, the colour of the shirts of each team is required, the colour ofthe goal keeper's shirts and that of the referee. However, it will beappreciated that other suitable colour templates and/or templatematching processes could be used.

Returning to FIG. 4, in step S50 the content processing workstation 10compares each of the pixels of each colour template with the pixelscorresponding to the shirt region of the image of the player. Thecontent processing workstation then generates a probability value thatindicates a similarity between pixels of the colour template and theselected pixels, to form a colour probability based on distance in huesaturation value (HSV) colour space from team and pitch colour models.In addition, a shape probability is used to localise the players, whichis based on correlation with a mean human shape model. Furthermore, amotion probability is based on distance from position predicted by arecursive least-squares estimator using starting position, velocity andacceleration parameters.

The creation of player masks is illustrated in FIG. 3A. FIG. 3A shows acamera view 210 of the football pitch 30 generated by the video camera20. As already explained, the pitch 30 forms part of the backgroundmodel, whilst the players 230, 232, 234, 236, 238, 240 should form partof the foreground mask as described above. Player bounding boxes areshown as the dotted lines around each player.

Thus far the steps S30, S40, S45 and S50 are performed with a respect tothe camera image processing. Having devised the foreground mask, playertracking is performed after first sorting the player tracks by proximityto the camera in step S55. Thus, the players which are identified asbeing closest to the camera are processed first in order to eliminatethese players from the tracking process. At step S60, player positionsare updated so as to maximise shape, colour and motion probabilities. Instep S70 an occlusion mask is constructed that excludes image regionsalready known to be covered by other closer player tracks. This ensuresthat players partially or wholly occluded by other players can only bematched to visible image regions. The occlusion mask improves trackingreliability as it reduces the incidence of track merging (whereby twotracks follow the same player after an occlusion event). This is aparticular problem when many of the targets look the same, because theycannot be (easily) distinguished by colour. The occlusion mask allowspixels to be assigned to a near player and excluded from the furtherplayer, preventing both tracks from matching to the same set of pixelsand thus maintaining their separate identities.

There then follows a process of tracking each player by extracting thefeatures provided within the camera image and mapping these onto a 3Dmodel as shown in FIGS. 3A and 3B. Thus, for corresponding a positionwithin the 2D image produced by the camera, a 3D position is assigned toa player which maximises shape, colour and motion probabilities. As willbe explained shortly, the selection and mapping of the player from the2D image onto the 3D model will be modified should an occlusion eventhave been detected. To assist the mapping from the 2D image to the 3Dmodel in step S65 the players to be tracked are initialised to theeffect that peaks in shape and colour probability are mapped onto themost appropriate selection of players. It should be emphasised that theinitialisation, which is performed at step S65 is only performed once,typically at the start of the tracking process. For a goodinitialisation of the system, the players should be well separated.After initialisation any errors in the tracking of the players arecorrected automatically in accordance with the present technique, whichdoes not require manual intervention.

In order to effect tracking in the 3D model from the 2D image positions,a transformation is effected by use of a projection matrix P. Trackingrequires that 2D image positions can be related to positions within the3D model. This transformation is accomplished by use of a projection (P)matrix. A point in 2D space equates to a line in 3D space:

$\begin{bmatrix}x \\y \\1\end{bmatrix} = {\begin{bmatrix}P_{00} & P_{01} & P_{02} & P_{03} \\P_{10} & P_{11} & P_{12} & P_{13} \\P_{20} & P_{21} & P_{22} & P_{23} \\0 & 0 & 0 & 1\end{bmatrix}\begin{bmatrix}x^{\prime} \\y^{\prime} \\z^{\prime} \\w\end{bmatrix}}$

A point in a 2D space equates to a line in a 3D space because a thirddimension, which is distance from the camera, is not known and thereforewould appear correspondingly as a line across the 3D model. A height ofthe objects (players) can be used to determined the distance from thecamera. A point in 3D space is gained by selecting a point along theline that lies at a fixed height above the known ground level (the meanhuman height). The projection matrix P is obtained a priori, once percamera before the match by a camera calibration process in whichphysical characteristics of the pitch such as the corners 31A, 31B, 31C,31D of the pitch 30, shown in FIG. 5A are used to determine the cameraparameters, which can therefore assist in mapping the 2D position of theplayers which have been identified onto the 3D model. This is a knowntechnique, using established methods. In terms of physical parameters,the projection matrix P incorporates the camera's zoom level, focalcentre, 3D position and 3D rotation vector (where it is pointing).

The tracking algorithm performed in step S60 is scalable and can operateon one or more cameras, requiring only that all points on the pitch arevisible from at least one camera (at a sufficient resolution).

In addition to the colour and shape matching, step S60 includes aprocess in which the motion of the player being tracked is also includedin order to correctly identified each of the players with a greaterprobability. Thus, the relevant movement of players between frames canbe determined both in terms of a relevant movement and in a direction.Thus, the relative motion can be used for subsequent frames to produce asearch region to identify a particular player. Furthermore, asillustrated in FIG. 5B, the three dimensional model of the footballpitch can be augmented with lines to 230.1, to 232.1, to 234.1, to236.1, to 238.1, 240.1 which are positioned relative to the graphicindication of the position of the players to reflect the relativedirection of motion of the players on the football pitch. Furtherexplanation of a technique for determining the orientation, providing arelative direction of motion of the player will be provided shortly.

At step S70, once the relative position of the players has beenidentified in the three dimensional model, then this position iscorrespondingly projected back into the 2D image view of the footballpitch and a relative bound is projected around the player identifiedfrom its position in the 3D model. Also at step S70, the relative boundaround the player is then added to the occlusion mask for that player.

FIG. 5B shows a plan view of a virtual model 220 of the football pitch.In the embodiment shown in FIG. 5B, the players 230, 232, and 234 (onthe left hand side of the pitch) have been identified by the contentprocessing workstation 10 as wearing a different coloured football shirtfrom the players 236, 238, and 240 (on the right hand side of the pitch)thus indicating that they are on different teams. Differentiating theplayers in this way makes the detection of each player after anocclusion event easier as they can easily be distinguished from eachother by the colour of their clothes.

Referring back to FIG. 4, at a step s60, the position of each player istracked using known techniques such as Kalman filtering although it willbe appreciated that other suitable techniques may be used. This trackingtakes place both in the camera view 210 and the virtual model 220. In anembodiment of the present invention, velocity prediction carried out bythe content processing workstation 10 using the position of the playersin the virtual model 220 is used to assist the tracking of each playerin the camera view 210.

Steps S60 and S70 are repeated until all players have been processed asrepresented by the decision box S75. Thus, if not all players have beenprocessed then processing proceeds to step S60 whereas if processing hasfinished then the processing terminates at S80.

As shown in FIG. 4, the method illustrated includes a further step S85,which may be required if images are produced by more than one camera. Assuch, the process steps S30 to S80 may be performed for the video imagesfrom each camera. As such, each of the players will be provided with adetection probability from each camera. Therefore, according to stepS85, each of the player's positions is estimated in accordance with theprobability for each player from each camera, and the position of theplayer estimated from the highest of the probabilities provided by eachcamera, so that the position with the highest probability for eachplayer is identified as the location for that player.

If it has been determined that an error has occurred in the tracking ofthe players on the football pitch then the track for that player can bere-initialised in step S90. The detection of an error in tracking isproduced where a probability of detection of a particular player isrelatively low for a particular track and accordingly, the track isre-initialised.

A result of performing the method illustrated in FIG. 4 is to generatepath data for each player, which provides a position of the player ineach frame of the video image, which represents a path that that playertakes throughout the match. Thus the path data provides position withrespect to time.

Our co-pending UK patent application number 0717277.8 discloses asolution to the separate technical problems of tracking players in theevent of an occlusion in which one player passes in front of another.Thus, when tracking the position of each player from a single cameraview if one player obscures a whole or part of another player, UK patentapplication 0717277.8 provides a disclosure of an arrangement formaintaining tracking information for both players until the ambiguity isresolved. One way in which the ambiguity can be resolved is to identifythe players using an automatic number recognition processing, such asthat disclosed in our co-pending UK patent application number 0717279.4.

Player Orientation

As mentioned above, embodiments of the present technique provide aprocess for estimating an object's orientation on a plane, such as aplayer's orientation on a field of play, from a two dimensional image ofthat player on the field of play generated by a camera. The orientationof the player on the field of play can be used to orientate asynthesised representation of that player on a three dimensional modelof the playing field. Thus embodiments of the present technique can beused generally to generate a three dimensional representation of ascene, which includes a plurality of objects disposed on a plane, thethree dimensional representation being generated from the video imagesof the scene. The video images include the objects on the plane producedfrom a view of the scene by a video camera. Thus for the example ofplayers playing on a football pitch, the players can be modelled andrepresented in a three dimensional model, which reflects as far aspossible a real football match captured on video images, by processingthose video images to generate tracking data, as explained above, andfrom that tracking data to generate for each player an orientation ofthat player on the football pitch, as explained below.

Determining the orientation of a player by means of image processing,would be computationally intensive, because it is necessary to compare amodel of the player, against the image region once for each possibleorientation. Furthermore this process is error-prone, because thefootage available is often low resolution, and there is a high posevariability of football players.

For these reasons orientation is determined heuristically, that is tosay, using high-level features provided by the tracking data for theplayers, described above for each frame. These features are the player'scurrent velocity v_(p), the current centre of attention C and thevelocity of the centre of attention v_(c). Additionally, the vectordisplacement d_(c) from the player to the attention centre is computed.Thus the tracking data for each individual player is combined with thetracking data collectively for all players on the pitch to generate aplayer orientation.

The centre of attention is the point at which the players are assumed tobe focused on (the focus of play); for football this would normally bethe position of the ball. However, because it is difficult to find theball, particularly with monocular imagery, using a single camera, anapproximation to the location of the ball is employed. According to thisapproximation, it is assumed that players will generally cluster aroundthe ball, and therefore the “centre of mass” COM of the all the players(excluding goalkeepers) is a good approximation to the attention centre.The centre of mass COM is marked with an X on FIGS. 5A and 5B. Thecentre of gravity is an analogous expression to the centre of gravity ofan object, which corresponds to the mean location on the plane of thepitch of all of the players. In practice this works well, generallyfailing only when there are abrupt turnovers (for example due to a goalkick or long pass), or game-stopping events (free kicks etc.)

Each player is assigned an orientation computed from three basisorientation vectors:

${O_{pm} = \frac{v_{p}}{v_{p}}},{O_{c\; m} = \frac{v_{c}}{v_{c}}},{O_{c} = \frac{d_{c}}{d_{c}}}$

Where O_(pm) is the orientation aligned to the player's direction ofmotion, O_(cm) is the orientation aligned to the attention centre'sdirection of motion and O_(cm) is the orientation directed towards theattention centre. Thus the basis function O_(cm) corresponds to therelative velocity and direction of motion of the attention centre.

Two blending factors are used to combine the three basis vectors in asmoothly-varying fashion, such that players are orientated towards thefocus of play, unless they are either very close to the centre of playor are moving quickly.

Motion Blending Factor:

$B_{m} = \left\{ \begin{matrix}{{{v_{p}} \geq T_{SH}},} & 1 \\{{T_{SL} < {v_{p}} < T_{SH}},} & \frac{{v_{p}} - T_{SL}}{T_{SH} - T_{SL}} \\{{{v_{p}} \leq T_{SL}},} & 0\end{matrix} \right.$

Where T_(SL)=low speed threshold and T_(SH)=high speed threshold.

Centre Blending Factor:

$B_{c} = \left\{ \begin{matrix}{{{d_{c}} \geq T_{DH}},} & 1 \\{{T_{DL} < {d_{c}} < T_{DH}},} & \frac{{d_{c}} - T_{DL}}{T_{DH} - T_{DL}} \\{{{d_{c}} \leq T_{DL}},} & 0\end{matrix} \right.$

Where T_(DL)=low distance threshold and T_(DH)=high distance threshold.

Using these blending factors the final player orientation O is computedas:O=B _(m) *O _(pm)+(1−B _(m))*(B _(c) *O _(c)+(1−B _(c))*O _(cm))

Note that the above blending equations implement a linear blend. Otherblending methods are available (for example sigmoid function), but it isnot clear that any significant improvement in results could be gained byusing these methods.

As for the example illustration shown in FIG. 5B, the player orientationvector O computed above, can be used to adjust the representation of therelative orientation of each of the players on the computer generatedmodel, as represented by the direction lines 230.1, 232.1, 234.1, 236.1,238.1, 240.1.

FIG. 6 provides three example representations of an orientation of asimplified synthetic model a human player for each of three sampledirections. Thus, for each player on the pitch the player orientationestimation module 42 generates a relative orientation of that player onthe football pitch using the algorithm of blending the basis factors asdescribed above. The representations in FIG. 6 are of so-called stickmodels, which represent the position of each of the limbs of theplayers. The relative orientations however, can be applied to morerealistic synthesised representations of each of the characters toproduce the three-dimensional model shown in FIG. 7 which is anotherexample of the representation shown in FIG. 5A. It will be appreciatedthat in one embodiment, the player orientation estimation module 42, canbe used without the player pose module 44. However, in combination theplayer orientation and player pose estimation provided by the twomodules 42, 44 serve to provide sufficient information from which arealistic synthesised model of the football match can be generated.

Player Pose

As mentioned above, embodiments of the present invention provide atechnique for estimating a pose of a human or animal body from videoimages taken of that body. As illustrated in FIG. 2, in one example thepose of a football player can be estimated by the player pose estimationmodule 44, although in other examples, the player pose may be estimatedwithout an estimation of the player orientation. As will be explainedbelow, embodiments of the present technique, provide a facility forautomatically detecting the pose of a player and for matching this poseto a synthesised model of that player. As such, a three dimensionalmodel representing the football match can be generated to model, as faras possible what is actually present in the video images of the realscene.

As explained above, the computationally complexity of using imageprocessing alone to match the pose of a player to that of a synthesisedmodel of a player is prohibitive, since the computational complexity forestimating the player pose is even greater then that for estimating theplayer orientation. This is because it is necessary to compare theplayer model (or part of player model) against image region containingthe player once for each possible pose of which there are many. Inaddition this process is prone to error, particularly because theresolution of the images of the player is low. Furthermore, the positionand orientation of the player, must be determined with a high level ofaccuracy as a prerequisite, whereas self-occlusion of players movingbehind other players other and deformation of clothing introduceadditional difficulties.

There are many known approaches to pose estimation by image processingin the academic literature. However, most require controlled studiocapture conditions, high-resolution imagery, multiple cameras (four ormore) and have unfeasibly high computational requirements. For example,Moeslund et al. 2006, “A survey of advances in vision-based human motioncapture and analysis” provides an overview of known techniques. Fortracking twenty three players simultaneously in real-time, inuncontrolled lighting conditions and using low-resolution imagery at theplayer level, the state-of-the-art methods are inadequate. Even with HDvideo, players at the far end of the pitch may occupy a region as smallas 15×30 pixels making player pose estimation particularly difficult.

As a result of the computational complexity of image processingtechniques, embodiments of the present technique employ a minimum ofimage processing, deriving the majority of pose information from meangait models. Gait, whether walking, running or sprinting, is a periodicpattern of motion that has been measured in a number of anatomicalstudies. These studies define, for normal human gait, joint rotationsmeasured at regular intervals over the gait cycle.

Examples of gait models are disclosed in Winter 1991, “The Biomechanicsand Motor Control of Human Gait: Normal, Elderly and Pathological”,Whittle et al. 1999, “Three-dimensional Relationships between theMovements of the Pelvis and Lumbar Spine during Normal Gait”, and Gardet al. 2004, “Comparison of kinematic and kinetic methods for computingthe vertical motion of the body centre of mass during walking”.

In one example, three mean gait models, G_(W), G_(R), G_(S), forwalking, running and sprinting gaits are constructed. Each of thesemodels define a rotation of the leg and arm joints, pelvis and torsorotations, and excursion of the body centre of mass along the vertical,horizontal and frontal axes. Thus each gait model G_(W), G_(R), G_(S),has a set of functions which define for any point in the gait cycle amotion degree for each of the hip, knee, ankle etc. An average gaitperiod (length in time of a single gait cycle, which is right heelstrike to right heel strike) is assigned to each model, with acorresponding player speed at which the model becomes active. We defineblending factors for the sprint model B_(S), running B_(R) and walkingB_(W) as follows:

$B_{S} = \left\{ {{\begin{matrix}{{{v_{p}} \geq S_{S}},} & \frac{v_{p}}{S_{S}} \\{{S_{R} < {v_{p}} < S_{S}},} & \frac{{v_{p}} - S_{R}}{S_{S} - S_{R}} \\{{{v_{p}} \leq S_{R}},} & 0\end{matrix}B_{R}} = \left\{ {{\begin{matrix}{{{v_{p}} \geq S_{S}},} & 0 \\{{S_{R} < {v_{p}} < S_{S}},} & \frac{S_{s} - {v_{p}}}{S_{S} - S_{R}} \\{{S_{W} < {v_{p}} < S_{R}},} & \sqrt{\frac{{v_{p}} - S_{W}}{S_{R} - S_{W}}} \\{{{v_{p}} \leq S_{W}},} & 0\end{matrix}B_{W}} = \left\{ \begin{matrix}{{{v_{p}} \geq S_{R}},} & 0 \\{{S_{W} < {v_{p}} < S_{R}},} & \left( {1 - \sqrt{\frac{{v_{p}} - S_{W}}{S_{R} - S_{W}}}} \right) \\{{{v_{p}} \leq S_{W}},} & \sqrt{\frac{v_{p}}{S_{W}}}\end{matrix} \right.} \right.} \right.$

The basis model G_(B) for a given player moving with velocity v_(p) iscomputed by blending between the two closest (in speed) gait models,using the above blending factors:G _(B) =B _(W) G _(W) +B _(R) G _(R) +B _(S) G _(S)

Note that the blending factors implement a linear transition betweenrunning and sprinting modes of gait, but the first two transitionsdiffer. The gait models are aiming to model stride rather than cadence.When moving from standing to walking, a square root is applied such thatthe transition is more rapid at the beginning. This has an advantage ofpreventing or at least reducing the appearance of skating or sliding ofthe synthesised models of the players on the pitch, represented by theblended gait model, when the player is moving at a very low speed, sothat the legs are seen to be moving soon after the player starts moving.Similarly, walking and running are distinctly different modes of gait,and so a linear transition is inappropriate. In this case, thetransition between the two is again made more abrupt by the use of thesquare root.

This basis gait model matches the player's motion to a generalpopulation average for their speed. A particular pose is selected byvarying the model phase, which defines the point in the gait cycle thatthe player currently occupies. The player model is animated by thefollowing phase update equation:θ_(t+1)=θ_(t) ±w

Where θ is the gait model phase, w is the frequency in radians per frameand t is the current frame number. The sign of the phase correction isdecided by the direction of gait, which is positive if the player ismoving forwards and negative if the player is moving backwards. Gaitdirection is decided according to the angle α between the player motionvector O_(pm) and the player orientation (facing) vector O:

${\alpha = {\cos^{- 1}\left( {O \cdot O_{pm}} \right)}},{{sign} = \left\{ \begin{matrix}{{\alpha \leq {0.5\;\pi}},} & + \\{{\alpha > {0.5\;\pi}},} & - \end{matrix} \right.}$

In order to match the generated player poses to the observed images,some image processing is required to determine a suitable phase offset(initial pose) for each frame. Given a suitable method of playertracking, such as that disclosed in co-pending UK patent applicationnumber 0717277.8, the position and size of the player is known. Alsoavailable is a foreground mask. The foreground mask defines, which imagepixels are likely to belong to a player. From the foreground mask thecurrent phase of the player's gait is estimated, as at phase=0 andphase=π the player's legs will be fully separated (heel-strike) as shownin FIG. 8, which correspond to left heel strike and right heel strike,and at phase=0.5π and 3/2π the legs will be together (crossing), asshown in FIG. 9. Thus 100% of the gait cycle will correspond to 2πradians.

In order to distinguish these between these gait phases a mask isconstructed to overlay the inner and outer regions of the player's legs.This is the mask shown in FIG. 10. An example of an isolating maskhaving inner and outer regions to isolate the player's legs is shown inFIG. 10, where (w, h)=(width, height) of the player bounding box.

The player tracking algorithm locates the centre of the player, which isnot necessarily aligned to the centre of the lower leg region,particularly for running and sprinting gaits. To remedy anymisalignment, the leftmost and rightmost foreground pixels within theouter mask are located, and a new centre is computed as the average ofthese two points. The mask is then shifted to this new centre, asillustrated in FIGS. 11( a) showing the position of the bounding boxbefore being adjusted, and 11(b) showing the position of the boundingbox after being shifted, for the players legs at full separation, and atFIGS. 11( c) and 11(d) for before and after the shift of the boundingbox for the players legs at minimum separation.

The next step is to count the sum total of pixels in the inner (S_(I))and outer (S_(O)) portions of the mask, and compute the ratio of outerto inner pixels (R_(OI)), normalised to a value between −1 and 1:

$R_{OI} = \frac{\left( {S_{O} - S_{I}} \right)}{\max\left( {S_{O},S_{I}} \right)}$

Plotting this ratio over time yields a time-varying sequence as shown inFIG. 12, by the line marked DG representing the detected gait.

As can be seen from the line DG shown in FIG. 12, a very noisymeasurement is produced due to shadows, occlusion by other players andthe crude approximation of leg sectors made by the mask.

A least-squares sinusoidal fit can be determined to provide the bestsinusoidal function matching the measured mask ratios, which isrepresented by the line marked with RG as the representative gaitapproximation. This process yields an optimal phase and amplitude for agiven frequency of sinusoid. However, this is not ideal, because anapproximate phase estimate has already been computed by adding the gaitfrequency to the phase found in the previous frame, and the amplitude ofthese ratio measurements is irrelevant. There is also no continuityconstraint in the fitting process, so it cannot guaranteed that thephase estimate for a given frame will be close to the phase computed forthe previous frame. Consequently, the current phase is estimated by useof gradient descent in a small neighbourhood about the predicted phaseθ_(t+1). This also results in lower computational requirements. If theinformation available is very poor for whatever reason, the phaseestimate is maintained uncorrected, so that the legs will still appearto move normally (although their pose in individual frames may not matchthe observed data).

It should be noted that the present technique cannot distinguish betweenleft leg forward and right leg forward (a phase shift of π in themodel); further processing is required to make this distinction. Also,from frontal viewpoints little or no pose information can be inferred,as the motion of the legs is not readily apparent. However, in this casethere is no problem, as the phase update equation ensures that theplayer's legs continue to move, and any error in phase alignment willnot be readily apparent to the viewer.

A further example illustrative representation of the relative gaitphase, which can be detected for a player from the video images of thatplayer is represented in FIGS. 13 and 14. As shown in FIG. 13, a plot ofa relative motion phase with a respect to phase of gait cycle is shownwith respect to a representation of an actual player as the player wouldbe shown from the video images, is shown in FIGS. 14( a), (b) and (c).Thus, FIG. 14( b) corresponds to the player with his/her legs togethercorresponding to a motion degree of phase π/2 and 3π/2, whereas Figures(a) and (c) correspond to the maximum motion displacement of the limbsin opposite directions according to zero and π radians respectively.Correspondingly, a gait cycle with a respect to degrees of motion graphis provided in FIG. 15 for an example synthesised character shown inFIG. 16( a), (b) and (c), which illustrates a relative gait phase for asynthesised character. Thus, as it will be appreciated FIGS. 13, 14, 15and 16 illustrate an operation of the player pose estimation module 44,which is arranged to:

-   -   Detect a gait phase of a particular player on the pitch as        represented from the video images;    -   From the video images a calculation of gait phase with respect        to gait cycle for a given frequency of the player's gait;    -   Generate a gait model and a corresponding gait cycle with        respect to motion degrees for a synthesised player; and    -   Match the gait phase cycle of the synthesised model to that of        the actual player in order to match the pose of the synthesised        model to that of the actual player present in the video images.

The player gait phase of the detected image and the gait phase of thesynthesised model are generated with respect to the motion of the lowerlimbs. As explained above, this is produced from a combination of motiondegrees with respect to percentage gait cycle for each of the gaitmodels which are walking, running and sprinting. Examples of knee, ankleand hip rotation phase from which the gait models G_(W), G_(R), G_(S),are formed are shown in FIGS. 17, 18 and 19. Thus, each of theserotation/cycle functions is used to form the gait models for therelative position of the lower limbs, which can be used to produce thegait cycle shown in FIG. 12.

FIGS. 20 and 21 illustrate a gait frequency transfer function showing arelationship between the player speed and gait frequency. FIG. 20provides an illustration of raw measurements produced from doing a leastsquares sinusoid fit to the frequency gait mask measurements whereasFIG. 21 produces a filtered representation of the gait model frequencytransfer function which defines the expected frequency for a givenspeed. With reference to the explanation provided above, it will beappreciated that the gait model frequency is half that of the sinusoidfilter fitted the mask measurements, which are shown in FIG. 12. This isa consequence of being unable to distinguish between left and right legforward from the mask measurements, which give an estimate of the anglebetween the legs rather than an estimate of the angle of each leg withrespect to the vertical axis.

As shown in FIGS. 20 and 21, each of three plateaus corresponding towalking, running and sprinting are shown for the variation in gaitfrequency for a given speed. As a result of the blending function, thereis a smooth transition between each of the respective gait models,walking, running and sprinting.

Example Illustration

FIGS. 22, 23A, 23B and 24 provide example illustrations of frames ofexample video images of a football match in which the present techniquehas been used to track players and produce a 3D model of the footballmatch as a virtual model. FIG. 22 provides an example illustration of avideo image produced by one HD camera of a football match. FIG. 23Aprovides an illustration of the video image of FIG. 22 in which theimage has been processed to produce the background only using the meanvalue of each pixel, and FIG. 23B provides an illustration of the videoimage of FIG. 22 in which the image has been processed to produce thebackground only using the variance of each pixel in the image. FIG. 24provides an illustration of a result of the tracking which is to providea bounded box around each player in correspondence with the exampleshown in FIG. 5A.

FIG. 25 provides a corresponding illustration in which two cameras havebeen used (such as the cameras 22.1, 22.2) to generate video images eachpositioned respectively to view a different half of the pitch. In boththe left half and the right half, the players are tracked as illustratedby the bounding boxes, which have been superimposed over each player.

In the lower half of FIG. 25, a virtual model of the football match hasbeen generated to represent the position of the players, as numbered inaccordance with their position on the pitch as viewed by the cameras inthe two dimensional video images in the upper half of FIG. 25. Thus the3D model view of the football match corresponds to the illustration ofthe virtual model shown in FIG. 5B or FIG. 7.

Tracking Overlaid on Live Video

According to the present technique tracking information, which isgenerated with respect to a 3D model of a 2D image of a football matchas described above, can be added to the video images captured by a videocamera. An example is illustrated in FIG. 26. As illustrated in FIG. 5B,the 3D model of the football pitch is used to assist in the tracking anddetection of the players on that football pitch. Once the relativeposition of the players have been detected from the 3D model then a maskfor that player is then projected onto the 2D image and used to assistin the detection and tracking of the players within the 2D image.However, once a player's position has been identified with a relativelyhigh probability then the position of that player within the 2D videoimage of the camera is known. Accordingly, a graphic illustrating anidentity of that player, as estimated by the tracking algorithm, can beoverlaid on to the live video feed from the camera by the contentprocessing workstation 10. Thus, as shown in FIG. 26, each of theplayers 300, 302, 304, 306 is provided with a corresponding label 308,310, 312, 314 which is then used to follow that player around the pitchin order to track the identity of that player. Thus, having tracked theobjects using the three dimensional model of the plane on which theobjects are disposed, the relative position of the identified objectsare projected back into the video images and a graphical label oridentifier or other effect introduced, so that identifiers of theplayers can be viewed in the live or processed video images.

Also shown within an image view in FIG. 26 are two sets of extractedimages 320, 322. Each of the sides on the football pitch is providedwith one of the sets of extracted images 320, 322. Each image is anisolated section of the image provided from the camera 20, which aims asfar as possible to isolate that player on the football pitch. Thus,having identified each of the players, then the image of that playerwithin the video image can be extracted and displayed with other playerswithin each of the sets corresponding to each of the teams on thefootball pitch. This presentation of the extracted images can provide anautomatic isolation of a view of a particular player without arequirement for a separate camera to track that player throughout thefootball match. Thus, a single camera can be used to capture the entirefootball pitch, and each of the players can be tracked throughout thematch as if the multiple cameras had been used to track each player. Asa result, a significant reduction in expense and system complexity canbe achieved.

Switching Between Real and Virtual Images

As explained above, with reference to FIGS. 5A and 5B, the process oftracking each of the players utilises a 3D model of the football pitchin order to assist in the identification and location of the players.Having gathered information as to an estimation of the position of theplayers and tracked that information between each of the frames of thevideo images (object path data), it is possible to create a virtualrepresentation of the live video images by synthesising images of eachof the players and representing those players within the 3D model.Furthermore, a relative position of a view of the model or synthesisedcamera position within the virtual space can be adjusted using knowntechniques to adapt the relative view of the 3D model of the footballpitch. Thus, for each of the positions of the players with respect totime as determined from the image view produced by the camera, it ispossible to recreate a virtual 3D view of that live football match froma desired position of the camera.

As illustrated in an example shown in FIG. 27, a 3D model has beensynthesised by applying the path data for each player to the 3D model(as illustrated in FIGS. 5B and 7) and the players have been representedby a model of each player at a position which changes with respect totime. Furthermore, since the view of the 3D model can be changed, arelative position of the camera can be altered in order to provide aview of the match at a position where in reality there is no camerapresent. Thus, as an example, if a free kick has been awarded, as shownin FIG. 27, the relative position of the camera can be provided frombehind the goal in order to provide a view of the free kick at aposition where in reality there is no camera present.

This is achieved as described above using the projection matrix P andmapping the relative position in 3D of the camera position from thecorners of the pitch. Furthermore, having estimated a relativeorientation of each player as described above, then this relativeorientation can also be provided with the path data for each player, andthe synthesised model of the player can be displayed with thatorientation, which will change with respect to time. Thus, theorientation of the player which is identified as part of the trackingprocess described above is provided with the path data to generate the3D model of the football match and this orientation data is used tochange the orientation of the player in the model as this corresponds tothe real image.

FIG. 21 provides a flow diagram indicating a summary of the methodaccording to the present invention. At step S100 the tracking dataindicating the movement and location of one or more of the characters isreceived. At step S101 a plurality of basis vectors is determined fromthe tracking data. At step S102 the basis vectors are combined inaccordance with the blending function, the blending function includingblending coefficients which determine a relative magnitude of each basisvector. At step S103 the orientation value is calculated from theblending function.

As will be appreciated, various modifications may be made to theembodiments described above without departing from the scope of thepresent invention as defined in the appended claims. For example,although the example embodiments have been illustrated with reference toa football match, it will be appreciated that any other sporting eventor entertainment event such as theatre could be viewed to apply thetechniques disclosed above. Furthermore, other processors other than theCell processor could be used to perform the technique. Processesaccording to the present technique, may be implemented in the form of acomputer program product comprising processor-implementable instructionsstored on a data carrier such as a floppy disk, optical disk, hard disk,PROM, RAM, flash memory or any combination of these or other storagemedia, or transmitted via data signals on a network such as an Ethernet,a wireless network, the internet, or any combination of these or othernetworks.

1. A method of estimating an orientation of one or more of a pluralityof objects disposed on a plane, from one or more video images of ascene, which includes the objects on the plane produced from a view ofthe scene by a video camera, the method comprising receiving for each ofthe one or more objects, object tracking data, which provides a positionof the object on the plane in the video images with respect to time,determining from the object tracking data a plurality of basis vectorsassociated with at least one of the objects, each basis vectorcorresponding to a factor, which can influence the orientation of theobject and each basis vector being related to the movement or locationof the one or more objects, and combining the basis vectors inaccordance with a blending function to calculate an estimate of theorientation of the object on the plane, the blending function includingblending coefficients which determine a relative magnitude of each basisvector used in the blending function.
 2. A method according to claim 1,wherein the basis vectors include a object velocity vector of theobject, derived from an estimated velocity of the one or more objectsdetermined from the object tracking data.
 3. A method according to claim1, wherein the basis vectors include a centre of attention vectordetermined by a centre of gravity of the estimated position of the oneor more objects derived from the object tracking data.
 4. A methodaccording to claim 3, wherein the basis vectors include a centre ofattention velocity vector determined by an estimated direction of motionof the centre of gravity.
 5. A method according to claim 1, wherein theblending coefficients include an object velocity coefficient set independence upon the estimated velocity of the one or more objects.
 6. Amethod according to claim 1, wherein the blending coefficients include acentre of attention distance coefficient set in dependence upon anestimated distance of the one or more objects from the centre ofattention derived from the object tracking data.
 7. A method accordingto claim 1, wherein the object tracking data is representative of athree dimensional estimation of the one or more objects' movement andlocation derived from a two dimensional representation of the one ormore objects in an environment.
 8. A method according to claim 1,comprising for each of the one or more objects performing the steps ofdetermining from the object tracking data a plurality of basis vectorsfor each object, and the combining of the basis vectors in accordancewith the blending function, to calculate an estimate of the orientationof each of the one or more objects on the plane.
 9. A method as claimedin claim 8, comprising generating a graphical model representing the oneor more objects on the plane, and combining the object tracking datawith the estimation of the orientation of the object, adjusting theposition of the one or more objects on the plane in accordance with theobject tracking data, and setting the orientation of the one or moreobjects on the plane in accordance with the estimation of theorientation of the object on the plane.
 10. A method as claimed in claim8 or 9, wherein the object path data is generated by processing thecaptured video images so as to extract one or more image features fromeach object, comparing the one or more image features with sample imagefeatures from a predetermined set of possible example objects which thevideo images may contain, identifying the objects from the comparison ofthe image features with the stored image features of the possibleexample objects, generating object path data, which includes objectidentification data for each object which identifies the respectiveobject; and provides a position of the object on the plane in the videoimages with respect to time; calculating a projection matrix forprojecting the position of each of the objects according to the objectpath data from the plane into a three dimensional model of the plane,for generating the three dimensional representation of the scene, byprojecting the position of the objects according to the object path datainto the plane of the three dimensional model of the scene using theprojection matrix.
 11. Computer software comprising program code storedon a non-transitory computer-readable storage medium, which, whenexecuted on a computer, executes a method according to claim
 1. 12. Anapparatus for estimating an orientation of one or more of a plurality ofobjects disposed on a plane, from one or more video images of a scene,which includes the objects on the plane produced from a view of thescene by a video camera, the apparatus including a data processoroperable to receive for each of the one or more objects, object trackingdata, which provides a position of the object on the plane in the videoimages with respect to time, to determine from the object tracking dataa plurality of basis vectors associated with at least one of theobjects, each basis vector corresponding to a factor, which caninfluence the orientation of the object and each basis vector beingrelated to the movement or location of the one or more objects, and tocombine the basis vectors in accordance with a blending function tocalculate an estimate of the orientation of the object on the plane, theblending function including blending coefficients which determine arelative magnitude of each basis vector used in the blending function.13. An apparatus according to claim 12, wherein the basis vectorsinclude a object velocity vector derived from an estimated velocity ofthe one or more objects determined from the object tracking data.
 14. Anapparatus according to any of claims 12 or 13, wherein the basis vectorsinclude a centre of attention vector determined by a centre of gravityof the estimated position of the one or more objects derived from theobject tracking data.
 15. An apparatus according to claim 14, whereinthe basis vectors include a centre of attention velocity vectordetermined by an estimated direction of motion of the centre of gravity.16. An apparatus according to claim 12, wherein the blendingcoefficients include: a object velocity coefficient set in dependence ofthe estimated velocity of the one or more objects, and a centre ofattention distance coefficient set in dependence of an estimateddistance of the one or more objects from the centre of attention derivedfrom the object tracking data.
 17. An apparatus according to any claim12, wherein the object tracking data is representative of a threedimensional estimation of the one or more objects' movement and locationderived from a two dimensional representation of the one or more objectsin an environment.
 18. An apparatus according to claim 12, wherein thedata processor is operable for each of the one or more objects todetermine from the object tracking data a plurality of basis vectors foreach object, and to combine the basis vectors in accordance with theblending function, to calculate an estimate of the orientation of eachof the one or more objects on the plane.
 19. An apparatus according toclaim 18, comprising a graphical processor operable to generate agraphical model representing the one or more objects on the plane, tocombine the object tracking data with the estimation of the orientationof the object, to adjust the position of the one or more objects on theplane in accordance with the object tracking data, and to set theorientation of the one or more objects on the plane in accordance withthe estimation of the orientation of the object on the plane.
 20. Anapparatus as claimed in claim 18 or 19, wherein the data processor isoperable to generate the object path data by processing the capturedvideo images so as to extract one or more image features from eachobject, comparing the one or more image features with sample imagefeatures from a predetermined set of possible example objects which thevideo images may contain, identifying the objects from the comparison ofthe image features with the stored image features of the possibleexample objects, generating object path data, which includes objectidentification data for each object which identifies the respectiveobject; and provides a position of the object on the plane in the videoimages with respect to time, and calculating a projection matrix forprojecting the position of each of the objects according to the objectpath data from the plane into a three dimensional model of the plane,for generating the three dimensional representation of the scene, byprojecting the position of the objects according to the object path datainto the plane of the three dimensional model of the scene using theprojection matrix.